In recent years, everyone talks about Big Data. We are always connected, so it is no mystery that the amount of data that is generated daily is scary. Throughout these years, a large number of experts and large firms have identified in the data that endless opportunities are generated: cost reduction, operational improvements, a better understanding of our customers or new business models are just some of the examples that new technologies related to Big Data can provide to us and our companies. Even we are talking about data monetization and publishing marketplaces where you can buy it, data has a doubtless economic value. Nowadays, knowledge is the key to competitive advantage so, whether your firm is customer-centric or product-centric, you will need data to be able to successfully compete with other companies in your sector. Therefore, data has unquestionably become an intangible asset, it is a resource that has a potential value to the business.
If you think of other resources in your company such as human or financial resources, you will quickly realize that all assets need to be managed. Think only of the human resources department or the tax department, they are full of workflows and operations to manage these assets since their treatment is critical for the proper functioning of the company. Every asset needs to be managed and data is not different. That’s why data management was born.
But what’s is exactly Data Management?
Extracting value from data does not happen by accident, it requires a well-defined and executed strategy, and to achieve that, the complete organization must be aligned, starting from the C-level and involving both technical (e.g. architects, developers, systems administrators…) and business roles (e.g. data stewards, data strategists…). The responsibility of data management is shared between business and IT so they have to collaborate. A clear definition of what Data Management is can be found on Data Management Body of Knowledge:
Data management is the set of plans, strategies, and policies that are designed, executed, and monitored in a firm whose objective is to extract, deliver, control, and protect the value of the data throughout its life cycle.
The main goals of data management are the following:
- Understand the information needs of the company, partners, employees, and customers, in addition to evaluating whether the data provided adds value to the business.
- Provide the technical means necessary to capture, securely store, and exploit information to ensure that all company needs are met.
- Pursue the highest integrity and quality through a continuous process of improvement over the stored data, since the quality of the decision-making process depends on it.
- Ensure the privacy and confidentiality of the data, as well as manage the accesses and prevent their inappropriate use. In other words, guarantee that the correct person sees the data they can see and no other.
Data Management in chapters
Once we’ve understood what Data Management is and what the main goals are, it’s time to divide it into smaller chapters to comprehend the different parts that compose Data Management and their main goals.
- Data architecture: identify enterprise data storage and process requirements. Strategically prepare organizations for evolving products and services to meet the long-term strategy of the company.
- Data modeling and design: design models and understand perspectives to align them with current and future business requirements.
- Data storage and operations: manage availability and performance and ensure integrity all over the data cycle.
- Data security: ensure privacy and confidentiality and avoid inappropriate access to enterprise data assets.
- Data integration and interoperability: provide data securely and when needed. Support Business Intelligence, Master Data Management, Analytics, and operations.
- Document and content: ensure integration capabilities between structured and unstructured content.
- Reference and master data: enable sharing of information assets across business domains in the organization and lower cost through standards and common data models.
- Data Warehousing and BI: build and maintain both technical and business environments to provide an integrated data perspective to support decision-making processes.
- Metadata: make metadata accessible within the organization and ensure its quality. Provide a clear insight into common business terms.
- Data quality: ensure data quality through the definition and implementation of processes to measure, monitor, improve, and report data quality an all the data lifecycle.
- Data governance: define and develop procedures, policies, and responsibilities for data management.
How much value my data have?
In this last section, I would like to discuss the value of the data. Although we have previously mentioned that data is the new asset that firms must begin to manage, there is still no specific measure for the value of the data. When we talk about the value of the data, we are talking about something that, to this day, continues to be very relative. The value of the data changes over time and even the value for one company may be different from the value for another.
Furthermore, we must remember that the ultimate objective of a profitable company is to generate benefits and, as such, value could be defined with the following formula:
Value = Benefit - Cost
Undoubtedly, there are a lot of variables at stake and unanswered questions, but if one thing is clear is that the trend in the coming years is that organizations will begin to manage their data, invest in the data management departments and it will create a great demand (higher than the current one) for professionals related to data, both technical and business. However, strategic changes do not happen overnight, but it is necessary to think carefully about what the strategy must be and the steps to execute it. The goal of the firm should be to become a data-driven firm, that is, that decision-making is made based on data. And, thanks to Data Management, the data will be available, secure, and with the best quality and integrity possible to guarantee that the decisions taken are as right as possible.
I would like to close this article with a clear example: Imagine if UBER did not know where their drivers are, how would they make the best decision? I just couldn’t, I could get a driver across town to pick up a passenger even if there was a driver right next door.
Originally published at https://www.datadriveninvestor.com on May 8, 2020.